Visualize time series numerical association rules
Author firefly-cpp
0 Stars
Updated Last
1 Year Ago
Started In
March 2023



NarmViz.jl is a Julia framework primarily developed to visualize time series numerical association rules. Framework also supports the visualization of the other numerical association rules.

Detailed insights

The current version includes (but is not limited to) the following functions:

  • loading datasets in CSV format,
  • preprocessing of data,
  • visualization of association rules,
  • exporting figures to files.

Visualization examples

Example 1 Example 2


pkg> add NarmViz


Basic run example

using NarmViz

# load transaction database
transactions = Transactions("datasets/random_sportydatagen.csv")

# basic settings for visualization
settings = Settings(
    all_features = false, # visualize all features, not only antecedents and consequence
    timeseries = false, # set false for non-time series datasets
    interval = "interval", # Name of the column which denotes the interval (only for time series datasets)
    antecedents = true, # visualize antecedents
    consequence = true, # visualize consequence
    antecedent_color = :blue, # color for showing the antecedent area
    consequence_color = :red, # color for showing consequence area
    title = "My first plot", # Title of visualization
    output_path = "visualization.pdf" # path

# vector of antecedents
antecedents = [
    Attribute("duration", 50, 65),
    Attribute("distance", 15, 40),

# vector of consequents
consequence = [
    Attribute("calories", 200, 450),
    Attribute("descent", 50, 140),

# call the primary function for visualization
# 3 denotes the interval; see the test dataset for an example
visualize(transactions, settings, 3, antecedents, consequence)

# use the following function call when dealing with non-time series data
# visualize(transactions, settings, antecedents, consequence)


Ideas are based on the following research papers:

[1] Fister Jr, I., Fister, I., Fister, D., Podgorelec, V., & Salcedo-Sanz, S. (2023). A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas. arXiv preprint arXiv:2302.12594.

[2] Fister Jr, I., Fister, D., Fister, I., Podgorelec, V., & Salcedo-Sanz, S. (2022). Time series numerical association rule mining variants in smart agriculture. arXiv preprint arXiv:2212.03669.

[3] I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020).

[4] I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. Differential evolution for association rule mining using categorical and numerical attributes In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.

Related software



This package is distributed under the MIT License. This license can be found online at


This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

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